Methods for diagnosing systemic lupus erythematosus

ABSTRACT

The invention relates to methods and materials involved in diagnosing SLE. More particularly, the invention relates to methods and materials involved in diagnosing SLE, diagnosing severe SLE, and assessing a mammal&#39;s susceptibility to develop severe SLE. For example, the invention provides nucleic acid arrays that can be used to diagnose SLE in a mammal. Such arrays can allow clinicians to diagnose SLE based on a simultaneous determination of the expression levels of many genes that are differentially expressed in SLE patients as compared to healthy controls.

CLAIM OF PRIORITY TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No. 10/222,202, filed on Aug. 16, 2002, now U.S. Pat. No. 7,118,865, the entire contents of which are hereby incorporated by reference.

STATEMENT AS TO FEDERALLY SPONSORED RESEARCH

Funding for the work described herein was provided in part by the National Institute of Arthritis and Musculoskeletal Diseases (grant no. NIH N01-AR-1-2256). The federal government may have certain rights in the invention.

BACKGROUND

1. Technical Field

The invention relates to methods and materials involved in diagnosing systemic lupus erythematosus (SLE). More particularly, the invention relates to methods and materials involved in diagnosing SLE, diagnosing severe SLE, and assessing a mammal's susceptibility to develop severe SLE.

2. Background Information

SLE is a chronic, inflammatory autoimmune disease characterized by the production of autoantibodies having specificity for a wide range of self-antigens. SLE autoantibodies mediate organ damage by directly binding to host tissues and by forming immune complexes that deposit in vascular tissues and activate immune cells. Organs targeted in SLE include the skin, kidneys, vasculature, joints, various blood elements, and the central nervous system (CNS). The severity of disease, the spectrum of clinical involvement, and the response to therapy vary widely among patients. This clinical heterogeneity makes it challenging to diagnose and manage lupus.

SUMMARY

The invention relates to methods and materials involved in diagnosing SLE. More particularly, the invention relates to methods and materials involved in diagnosing SLE, diagnosing severe SLE, and assessing a mammal's susceptibility to develop severe SLE. For example, the invention provides nucleic acid arrays that can be used to diagnose SLE in a mammal. Such arrays can allow clinicians to diagnose SLE based on a determination of the expression levels of many genes that are differentially expressed in SLE patients as compared to healthy controls.

In addition, the invention provides methods and materials involved in diagnosing SLE conditions that are accompanied by activation of an interferon pathway. For the purpose of this invention, the term “SLE accompanied by activation of an interferon pathway” (abbreviated “SLE-AIP”) refers to any SLE condition that coexists with or is caused by activation of an interferon pathway. Activation of an interferon pathway refers to a state where interferon-regulated genes that are up-regulated in response to interferon are up-regulated and where interferon-regulated genes that are down-regulated in response to interferon are down-regulated. Typically, activation of an interferon pathway results in the presence of a gene expression profile that is similar to the gene expression profile observed in cells that were treated with interferon. An interferon pathway can be activated regardless of the presence or absence of detectable levels of interferon. For example, an SLE patient can have low levels of detectable interferon while exhibiting a gene expression profile characteristic of an activated interferon pathway. Such an SLE patient can be diagnosed as having SLE-AIP.

Diagnosing patients as having SLE-AIP can help clinicians determine appropriate treatments for those patients. For example, a clinician who diagnoses a patient as having SLE-AIP can treat that patient with medication that improves both the patient's SLE symptoms and aberrant activation of an interferon pathway. In some cases, a single medication can be used to reverse a patient's activation of an interferon pathway such that the patient's SLE symptoms are reduced or relieved. Thus, treating a patient having SLE-AIP by modulating the level of interferon pathway activation can improve that patient's health and quality of life by, for example, reducing the symptoms associated with SLE.

Typically, a diagnosis of SLE can be made on the basis of 11 criteria defined by the American College of Rheumatology (ACR). These criteria include malar rash, discoid rash, photosensitivity, oral ulcers, arthritis, serositis, renal disorder, neurologic disorder, hematologic disorder, immunologic disorder, and antinuclear antibody (Tan et al. (1982) Arthritis Rheum. 25:1271-1277). A mammal (e.g., a human) can be clinically diagnosed with SLE if be or she meets at least four of the eleven criteria. The term “severe SLE” as used herein refers to an SLE condition where the patient has one or more of the following: renal, central nervous system, or hematologic involvement.

The invention is based on the discovery of genes that are differentially expressed between SLE patients and healthy controls. The invention also is based on the discovery that the expression levels of these genes can be used to distinguish mammals with SLE from healthy mammals. For example, the expression levels for the genes listed in Table I can be assessed to diagnose SLE. In addition, the invention is based on the discovery that a portion of SLE patients can have SLE associated with or caused by activation of an interferon pathway. For example, SLE patients having severe SLE can be, at least partially, dependent upon the presence of an activated interferon pathway. Further, the invention is based on the discovery of genes that are differentially expressed between SLE-AIP patients and SLE patients not associated with an activated interferon pathway. For example, the expression levels for the genes listed in Table IV can be assessed to diagnose SLE-AIP.

In one aspect, the invention provides a method for diagnosing severe systemic lupus erythematosus. The method can involve determining whether or not a mammal contains cells that express at least 2 of the genes listed in Table 4 to an extent greater than or less than the average level of expression exhibited in control cells from one or more control mammals, wherein the mammal and the one or more control mammals are from the same species, and diagnosing the mammal as having severe systemic lupus erythematosus if the mammal contains the cells and diagnosing the mammal as not having severe systemic lupus erythematosus if the mammal does not contain the cells. The mammal can be a human. The control mammals can be healthy humans or humans with mild systemic lupus erythematosus. The cells and the control cells can be peripheral blood mononuclear cells. The method can involve determining whether or not the mammal contains cells that express at least 5 or at least 10 of the genes to an extent greater than or less than the level of expression exhibited in the control cells. The extent can be less than the average level of expression exhibited in control cells from at least 10 or at least 20 control mammals. The determining step can involve measuring the level of mRNA expressed from the at least 2 of the genes.

In another aspect, the invention provides a method for assessing the predisposition of a mammal to develop severe systemic lupus erythematosus. The method can involve determining whether or not the mammal contains cells that express at least 2 of the genes listed in Table 4 to an extent greater than or less than the average level of expression exhibited in control cells from one or more control mammals, wherein the mammal and the one or more control mammals are from the same species, and classifying the mammal as being susceptible to develop severe systemic lupus erythematosus if the mammal contains the cells and classifying the mammal as not being susceptible to develop severe systemic lupus erythematosus if the mammal does not contain the cells. The mammal can be a human. The control mammals can be healthy humans. The cells and the control cells can be peripheral blood mononuclear cells. The method also can involve determining whether or not the mammal contains cells that express at least 5 or at least 10 of the genes to an extent greater than or less than the level of expression exhibited in the control cells. The method can involve determining whether or not the mammal contains cells that express at least 10 of the genes to an extent greater than or less than the level of expression exhibited in the control cells. The extent can be greater than or less than the average level of expression exhibited in control cells from at least 10 or at least 20 control mammals. The determining step can involve measuring the level of mRNA expressed from said at least 5 of the genes.

In another aspect, the invention provides a method for diagnosing systemic lupus erythematosus in a mammal. The method can involve determining whether or not the mammal contains cells that express at least 10 of the genes listed in Table 1 to an extent greater than or less than the average level of expression exhibited in control cells from one or more control mammals, wherein the mammal and the one or more control mammals are from the same species, and diagnosing the mammal as having systemic lupus erythematosus if the mammal contains the cells and diagnosing the mammal as not having systemic lupus erythematosus if the mammal does not contain the cells.

In yet another aspect, the invention provides a method for diagnosing systemic lupus erythematosus in a mammal. The method can involve determining whether or not the mammal contains cells that express at least 5 of the genes listed in Table 2 to an extent greater than the average level of expression exhibited in control cells from one or more control mammals, wherein the mammal and the one or more control mammals are from the same species, and diagnosing the mammal as having systemic lupus erythematosus if the mammal contains the cells and diagnosing said mammal as not having systemic lupus erythematosus if the mammal does not contain the cells.

The invention also provides a method for diagnosing systemic lupus erythematosus in a mammal. The method can involve determining whether or not the mammal contains cells that express at least 5 of the genes listed in Table 3 to an extent less than the average level of expression exhibited in control cells from one or more control mammals, wherein the mammal and the one or more control mammals are from the same species, and diagnosing the mammal as having systemic lupus erythematosus if the mammal contains the cells and diagnosing the mammal as not having systemic lupus erythematosus if the mammal does not contain the cells.

In another aspect, the invention provides a nucleic acid array containing at least 20 nucleic acid molecules. Each of the at least 20 nucleic acid molecules can have a different nucleic acid sequence, and at least 50 percent of the nucleic acid molecules of the array can contain a sequence from a gene selected from the group consisting of the genes listed in Table 1. The array can contain at least 50 nucleic acid molecules, and each of the at least 50 nucleic acid molecules can have a different nucleic acid sequence. The array can contain at least 100 nucleic acid molecules, and each of the at least 100 nucleic acid molecules can have a different nucleic acid sequence. Each of the nucleic acid molecules that contain a sequence from a gene selected from the group can contain no more than three mismatches. At least 75 percent or at least 95 percent of the nucleic acid molecules of the array can contain a sequence from a gene selected from the group. The array can contain glass.

In yet another aspect, the invention provides a nucleic acid array containing at least 5 nucleic acid molecules. Each of the at least 5 nucleic acid molecules can have a different nucleic acid sequence, and at least 50 percent of the nucleic acid molecules of the array can contain a sequence from a gene selected from the group consisting of the genes listed in Table 4. The array can contain at least 10 nucleic acid molecules, and each of the at least 10 nucleic acid molecules can have a different nucleic acid sequence. The array can contain at least 20 nucleic acid molecules, and each of the at least 20 nucleic acid molecules can have a different nucleic acid sequence. Each of the nucleic acid molecules that contain a sequence from a gene selected from the group can contain no more than three mismatches. At least 75 percent or at least 95 percent of the nucleic acid molecules of the array can contain a sequence from a gene selected from the group. The array can contain glass.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. Although methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, suitable methods and materials are described below. All publications, patent applications, patents, and other references mentioned herein are incorporated by reference in their entirety. In case of conflict, the present specification, including definitions, will control. In addition, the materials, methods, and examples are illustrative only and not intended to be limiting.

Other features and advantages of the invention will be apparent from the following detailed description, and from the claims.

DESCRIPTION OF DRAWINGS

FIG. 1 is a graph plotting the IFN scores that were calculated for SLE patients and control subjects using the normalized expression levels of the 14 IFN-regulated genes that comprise the IFN signature; p=2.8×10⁻⁷.

FIG. 2 is a graph plotting the number of SLE criteria observed in the 24 SLE patients with the highest IFN scores and in the 24 SLE patients with the lowest IFN scores; p=0.002.

FIG. 3 is a graph plotting the number of SLE criteria met by each patient against the IFN score of each patient.

FIG. 4 is a bar graph showing the percent of patients in the IFN-high and IFN-low groups with ACR-defined criteria for renal and/or CNS disease (p=7.7×10⁻⁶) or hematologic involvement (p=6.1×10⁻⁹).

DETAILED DESCRIPTION

The invention provides to methods and materials involved in diagnosing SLE. More particularly, the invention relates to methods and materials involved in diagnosing SLE, diagnosing severe SLE, and assessing a mammal's susceptibility to develop severe SLE. For example, the invention provides nucleic acid arrays that can be used to diagnose SLE, severe SLE, and/or SLE-AIP in a mammal. Such arrays can allow clinicians to diagnose SLE, severe SLE, and/or SLE-AIP based on a determination of the expression levels of many genes that are differentially expressed.

1. Diagnosing SLE

The invention provides methods for diagnosing a mammal (e.g., a human) as having SLE. In one embodiment, a mammal can be diagnosed as having SLE if it is determined that the mammal contains cells that express one or more of the genes listed in Table 1 at a level that is greater or less than the average level of expression of the same one or more genes observed in control cells obtained from control mammals. In another embodiment, a mammal can be diagnosed as having SLE if it is determined that the mammal contains cells that express one or more of the genes listed in Table 2 at a level that is greater than the average level of expression of the same one or more genes observed in control cells obtained from control mammals. In yet another embodiment, a mammal can be diagnosed as having SLE if it is determined that the mammal contains cells that express one or more of the genes listed in Table 3 at a level that is less than the average level of expression of the same one or more genes observed in control cells obtained from control mammals.

The mammal can be any mammal such as a human, dog, mouse, or rat. Any cell type can be isolated and evaluated. For example, peripheral blood mononuclear cells (PBMC), total white blood cells, lymph node cells, spleen cells, or tonsil cells can be isolated from a human patient and evaluated to determine if that patient contains cells that (1) express one or more of the genes listed in Table 1 at a level that is greater or less than the average level of expression observed in control cells, (2) express one or more of the genes listed in Table 2 at a level that is greater than the average level of expression observed in control cells, or (3) express one or more of the genes listed in Table 3 at a level that is less than the average level of expression observed in control cells. The expression of any number of the genes listed in Tables 1, 2, or 3 can be evaluated to diagnose SLE. For example, the expression of one or more than one (e.g., two, three, four, five, six, seven, eight, nine, ten, 15, 20, 25, 30, or more than 30) of the genes listed in Table 1, 2, or 3 can be used.

The expression level can be greater than or less than the average level observed in control cells obtained from control mammals. Typically, a gene can be classified as being expressed at a level that is greater than or less than the average level observed in control cells if the expression levels differ by at least 1-fold (e.g., 1.5-fold, 2-fold, 3-fold, or more than 3-fold). In addition, the control cells typically are the same type of cells as those isolated from the mammal being evaluated. In some cases, the control cells can be isolated from one or more mammals that are from the same species as the mammal being evaluated. When diagnosing SLE, the control cells can be isolated from healthy mammals such as healthy humans who do not have SLE. Any number of control mammals can be used to obtain the control cells. For example, control cells can be obtained from one or more healthy mammals (e.g., at least 5, at least 10, at least 15, at least 20, or more than 20 control mammals).

Any method can be used to determine whether or not a specific gene is expressed at a level that is greater or less than the average level of expression observed in control cells. For example, the level of expression from a particular gene can be measured by assessing the level of mRNA expression from the gene. Levels of mRNA expression can be evaluated using, without limitation, northern blotting, slot blotting, quantitative reverse transcriptase polymerase chain reaction (RT-PCR), or chip hybridization techniques. Methods for chip hybridization assays include, without limitation, those described herein. Such methods can be used to determine simultaneously the relative expression levels of multiple mRNAs. Alternatively, the level of expression from a particular gene can be measured by assessing polypeptide levels. Polypeptide levels can be measured using any method such as immuno-based assays (e.g., ELISA), western blotting, or silver staining.

TABLE 1 Genes with expression levels that differ between SLE patients and normal controls Accession No. Gene U60060 fasciculation and elongation protein zeta 1 (zygin I) AF057036 collagen-like tail subunit (single strand of homotrimer) of asymmetric acetylcholinesterase M93107 3-hydroxybutyrate dehydrogenase (heart, mitochondrial) U14575 protein phosphatase 1, regulatory (inhibitor) subunit 8 X15882 collagen VI alpha-2 C-terminal globular domain S68805 glycine amidinotransferase (L-arginine:glycine amidinotransferase) U75744 deoxyribonuclease I-like 3 AF091071 similar to S. cerevisiae RER1 AI651806 cysteine-rich motor neuron 1 AB028994 KIAA1071 protein S75168 megakaryocyte-associated tyrosine kinase X73617 T cell receptor delta locus X07730 kallikrein 3, (prostate specific antigen) AF009787 T cell receptor beta locus M21624 T cell receptor delta locus AB009598 beta-1,3-glucuronyltransferase 3 (glucuronosyltransferase I) AL021154 E2F transcription factor 2 L25444 TAF6 RNA polymerase II, TATA box binding protein (TBP)-associated factor, 80 kD AJ001383 lymphocyte antigen 94 homolog, activating NK-receptor; NK-p46 (mouse) U75370 polymerase (RNA) mitochondrial (DNA directed) AL049365 DKFZp586A0618 M16801 nuclear receptor subfamily 3, group C, member 2 M28827 CD1C antigen, c polypeptide U51712 hypothetical protein SMAP31 X66079 Spi-B transcription factor (Spi-1/PU.1 related) U11276 killer cell lectin-like receptor subfamily B, member 1 M36881 lymphocyte-specific protein tyrosine kinase M31523 transcription factor 3 (E2A immunoglobulin enhancer binding factors E12/E47) M26062 interleukin 2 receptor, beta AF026031 putative mitochondrial outer membrane protein import receptor AB011115 KIAA0543 protein AF041261 leukocyte immunoglobulin-like receptor, subfamily A (without TM domain), member 4 D55716 MCM7 minichromosome maintenance deficient 7 (S. cerevisiae) L04282 zinc finger protein 148 (pHZ-52) AJ001687 DNA segment on chromosome 12 (unique) 2489 expressed sequence AI524873 like mouse brain protein E46 U76421 adenosine deaminase, RNA-specific, B1 (homolog of rat RED1) AF031137 lymphocyte antigen 117 X59871 transcription factor 7 (T-cell specific, HMG-box) U43408 tyrosine kinase, non-receptor, 1 AB018289 KIAA0746 protein AI761647 IMAGE-2370113 M18737 granzyme A (granzyme 1, cytotoxic T-lymphocyte-associated serine esterase 3) AB023220 ubiquitin specific protease 20 W26633 melanoma antigen, family D, 1 M68892 integrin, beta 7 AJ236885 zinc finger protein 148 (pHZ-52) L13858 son of sevenless (Drosophila) homolog 2 AF094481 CGG triplet repeat binding protein 1 M28215 RAB5A, member RAS oncogene family U43083 guanine nucleotide binding protein (G protein), q polypeptide X02344 tubulin, beta, 2 M22324 alanyl (membrane) aminopeptidase (aminopeptidase N, aminopeptidase M, microsomal aminopeptidase, CD13, p150) Y07566 Ric-like, expressed in many tissues (Drosophila) U50553 DEAD/H (Asp-Glu-Ala-Asp/His) box polypeptide 3 X54134 protein tyrosine phosphatase, receptor type, E L40388 thyroid receptor interacting protein 15 L19872 aryl hydrocarbon receptor U78107 N-ethylmaleimide-sensitive factor attachment protein, gamma AL050272 DKFZP566B183 protein U56998 cytokine-inducible kinase AI189226 RAB31, member RAS oncogene family Z50781 delta sleep inducing peptide, immunoreactor S87759 protein phosphatase 1A (formerly 2C), magnesium-dependent, alpha isoform U88629 ELL-RELATED RNA POLYMERASE II, ELONGATION FACTOR AF006513 chromodomain helicase DNA binding protein 1 AI138605 hypothetical protein DKFZp566A1524 L16794 MADS box transcription enhancer factor 2, polypeptide D (myocyte enhancer factor 2D) AL080235 Ras-induced senescence 1 L17418 complement component (3b/4b) receptor 1, including Knops blood group system Y00816 complement component (3b/4b) receptor 1, including Knops blood group system M63835 Fc fragment of IgG, high affinity Ia, receptor for (CD64) L13943 glycerol kinase U89278 early development regulator 2 (homolog of polyhomeotic 2) U58334 tumor protein p53 binding protein, 2 X54134 protein tyrosine phosphatase, receptor type, E X59834 glutamate-ammonia ligase (glutamine synthase) AL047596 capicua homolog (Drosophila) AB023211 peptidyl arginine deiminase, type II D43945 transcription factor EC U79273 clone 23933 Z18956 solute carrier family 6 (neurotransmitter transporter, taurine), member 6 Y10313 interferon-related developmental regulator 1 AF004849 homeodomain interacting protein kinase 3 AI808958 KIAA0870 protein U47634 tubulin, beta, 4 X55988 ribonuclease, RNase A family, 2 (liver, eosinophil-derived neurotoxin) W29030 CGI-49 protein U12471 thrombospondin-1 AF013591 sudD (suppressor of bimD6, Aspergillus nidulans) homolog X52015 interleukin 1 receptor antagonist M16967 coagulation factor V (proaccelerin, labile factor) U57094 RAB27A, member RAS oncogene family U66711 lymphocyte antigen 6 complex, locus E AA521060 IMAGE-826408 X68090 IgG Fc receptor class IIA Y08136 acid sphingomyelinase-like phosphodiesterase AL049685 hypothetical protein similar to small G proteins, especially RAP-2A L28957 phosphate cytidylyltransferase 1, choline, alpha isoform Z22576 CD69 antigen (p60, early T-cell activation antigen) U41766 a disintegrin and metalloproteinase domain 9 (meltrin gamma) M57230 interleukin 6 signal transducer (gp130, oncostatin M receptor) X17094 paired basic amino acid cleaving enzyme (furin, membrane associated receptor protein) AC005192 interferon-related developmental regulator 1 AI547258 metallothionein 2A L22075 guanine nucleotide binding protein (G protein), alpha 13 U22431 hypoxia-inducible factor 1, alpha subunit (basic helix-loop-helix transcription factor) AB006746 phospholipid scramblase 1 AF030196 stannin AA010078 H4 histone family, member D X56807 desmocollin 2 AL080156 DKFZP434J214 protein AF017257 v-ets erythroblastosis virus E26 oncogene homolog 2 (avian) AL049340 DKFZp564P056 M24283 intercellular adhesion molecule 1 (CD54), human rhinovirus receptor D49817 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 3 AF016903 agrin U77914 jagged 1 (Alagille syndrome) M33882 myxovirus (influenza) resistance 1, homolog of murine (interferon- inducible protein p78) U68385 Meis1, myeloid ecotropic viral integration site 1 homolog 3 (mouse) L05515 cAMP response element-binding protein CRE-BPa U15555 serine palmitoyltransferase, long chain base subunit 2 L42025 HIV-1 Rev binding protein X07834 superoxide dismutase 2, mitochondrial D90144 small inducible cytokine A3 M13755 interferon-stimulated protein, 15 kDa M83670 carbonic anhydrase IV M55047 synaptotagmin I U91512 ninjurin 1 AB008775 aquaporin 9 X79535 tubulin, beta polypeptide J04102 v-ets erythroblastosis virus E26 oncogene homolog 2 (avian) D10040 fatty-acid-Coenzyme A ligase, long-chain 2 AW044649 sin3-associated polypeptide, 30 kD X03473 H1 histone family, member 0 AB007448 solute carrier family 22 (organic cation transporter), member 4 Z14138 mitogen-activated protein kinase kinase kinase 8 X02419 uPA U10473 UDP-Gal:betaGlcNAc beta 1,4-galactosyltransferase, polypeptide 1 AI679353 solute carrier family 11 (proton-coupled divalent metal ion transporters), member 1 AA203213 interferon-stimulated protein, 15 kDa AB018259 KIAA0716 gene product AF055993 sin3-associated polypeptide, 30 kD X54486 serine (or cysteine) proteinase inhibitor, clade G (C1 inhibitor), member 1 AJ225089 2′-5′-oligoadenylate synthetase-like AL022318 similar to APOBEC1 S59049 regulator of G-protein signaling 1 Y10032 serum/glucocorticoid regulated kinase AI924594 tetraspan 2 D21205 zinc finger protein 147 (estrogen-responsive finger protein) U37707 membrane protein, palmitoylated 3 (MAGUK p55 subfamily member 3) L40387 2′-5′-oligoadenylate synthetase-like X78711 glycerol kinase D10923 putative chemokine receptor; GTP-binding protein AW006742 IMAGE-2489058 AL109730 EUROIMAGE 68600 X99699 XIAP associated factor-1 AB000115 hypothetical protein, expressed in osteoblast L13210 lectin, galactoside-binding, soluble, 3 binding protein U22970 interferon, alpha-inducible protein (clone IFI-6-16) U96721 Hermansky-Pudlak syndrome L10126 activin A receptor, type IB S62138 TLS/CHOP M33684 protein tyrosine phosphatase, non-receptor type 1 M63978 vascular endothelial growth factor X89101 tumor necrosis factor receptor superfamily, member 6 M60278 diphtheria toxin receptor (heparin-binding epidermal growth factor-like growth factor) X59770 interleukin 1 receptor, type II X04500 interleukin 1, beta D30783 epiregulin U43774 Fc fragment of IgA, receptor for

TABLE 2 Genes from Table 1 that are higher in SLE patients as compared to controls Accession No. Gene L13858 son of sevenless (Drosophila) homolog 2 AF094481 CGG triplet repeat binding protein 1 M28215 RAB5A, member RAS oncogene family U43083 guanine nucleotide binding protein (G protein), q polypeptide X02344 tubulin, beta, 2 M22324 alanyl (membrane) aminopeptidase (aminopeptidase N, aminopeptidase M, microsomal aminopeptidase, CD13, p150) Y07566 Ric-like, expressed in many tissues (Drosophila) U50553 DEAD/H (Asp-Glu-Ala-Asp/His) box polypeptide 3 X54134 protein tyrosine phosphatase, receptor type, E L40388 thyroid receptor interacting protein 15 L19872 aryl hydrocarbon receptor U78107 N-ethylmaleimide-sensitive factor attachment protein, gamma AL050272 DKFZP566B183 protein U56998 cytokine-inducible kinase AI189226 RAB31, member RAS oncogene family Z50781 delta sleep inducing peptide, immunoreactor S87759 protein phosphatase 1A (formerly 2C), magnesium-dependent, alpha isoform U88629 ELL-RELATED RNA POLYMERASE II, ELONGATION FACTOR AF006513 chromodomain helicase DNA binding protein 1 AI138605 hypothetical protein DKFZp566A1524 L16794 MADS box transcription enhancer factor 2, polypeptide D (myocyte enhancer factor 2D) AL080235 Ras-induced senescence 1 L17418 complement component (3b/4b) receptor 1, including Knops blood group system Y00816 complement component (3b/4b) receptor 1, including Knops blood group system M63835 Fc fragment of IgG, high affinity Ia, receptor for (CD64) L13943 glycerol kinase U89278 early development regulator 2 (homolog of polyhomeotic 2) U58334 tumor protein p53 binding protein, 2 X54134 protein tyrosine phosphatase, receptor type, E X59834 glutamate-ammonia ligase (glutamine synthase) AL047596 capicua homolog (Drosophila) AB023211 peptidyl arginine deiminase, type II D43945 transcription factor EC U79273 clone 23933 Z18956 solute carrier family 6 (neurotransmitter transporter, taurine), member 6 Y10313 interferon-related developmental regulator 1 AF004849 homeodomain interacting protein kinase 3 AI808958 KIAA0870 protein U47634 tubulin, beta, 4 X55988 ribonuclease, RNase A family, 2 (liver, eosinophil-derived neurotoxin) W29030 CGI-49 protein U12471 thrombospondin-1 AF013591 sudD (suppressor of bimD6, Aspergillus nidulans) homolog X52015 interleukin 1 receptor antagonist M16967 coagulation factor V (proaccelerin, labile factor) U57094 RAB27A, member RAS oncogene family U66711 lymphocyte antigen 6 complex, locus E AA521060 IMAGE-826408 X68090 IgG Fc receptor class IIA Y08136 acid sphingomyelinase-like phosphodiesterase AL049685 hypothetical protein similar to small G proteins, especially RAP-2A L28957 phosphate cytidylyltransferase 1, choline, alpha isoform Z22576 CD69 antigen (p60, early T-cell activation antigen) U41766 a disintegrin and metalloproteinase domain 9 (meltrin gamma) M57230 interleukin 6 signal transducer (gp130, oncostatin M receptor) X17094 paired basic amino acid cleaving enzyme (furin, membrane associated receptor protein) AC005192 interferon-related developmental regulator 1 AI547258 metallothionein 2A L22075 guanine nucleotide binding protein (G protein), alpha 13 U22431 hypoxia-inducible factor 1, alpha subunit (basic helix-loop-helix transcription factor) AB006746 phospholipid scramblase 1 AF030196 stannin AA010078 H4 histone family, member D X56807 desmocollin 2 AL080156 DKFZP434J214 protein AF017257 v-ets erythroblastosis virus E26 oncogene homolog 2 (avian) AL049340 DKFZp564P056 M24283 intercellular adhesion molecule 1 (CD54), human rhinovirus receptor D49817 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 3 AF016903 agrin U77914 jagged 1 (Alagille syndrome) M33882 myxovirus (influenza) resistance 1, homolog of murine (interferon- inducible protein p78) U68385 Meis1, myeloid ecotropic viral integration site 1 homolog 3 (mouse) L05515 cAMP response element-binding protein CRE-BPa U15555 serine palmitoyltransferase, long chain base subunit 2 L42025 HIV-1 Rev binding protein X07834 superoxide dismutase 2, mitochondrial D90144 small inducible cytokine A3 M13755 interferon-stimulated protein, 15 kDa M83670 carbonic anhydrase IV M55047 synaptotagmin I U91512 ninjurin 1 AB008775 aquaporin 9 X79535 tubulin, beta polypeptide J04102 v-ets erythroblastosis virus E26 oncogene homolog 2 (avian) D10040 fatty-acid-Coenzyme A ligase, long-chain 2 AW044649 sin3-associated polypeptide, 30 kD X03473 H1 histone family, member 0 AB007448 solute carrier family 22 (organic cation transporter), member 4 Z14138 mitogen-activated protein kinase kinase kinase 8 X02419 uPA U10473 UDP-Gal:betaGlcNAc beta 1,4-galactosyltransferase, polypeptide 1 AI679353 solute carrier family 11 (proton-coupled divalent metal ion transporters), member 1 AA203213 interferon-stimulated protein, 15 kDa AB018259 KIAA0716 gene product AF055993 sin3-associated polypeptide, 30 kD X54486 serine (or cysteine) proteinase inhibitor, clade G (C1 inhibitor), member 1 AJ225089 2′-5′-oligoadenylate synthetase-like AL022318 similar to APOBEC1 S59049 regulator of G-protein signalling 1 Y10032 serum/glucocorticoid regulated kinase AI924594 tetraspan 2 D21205 zinc finger protein 147 (estrogen-responsive finger protein) U37707 membrane protein, palmitoylated 3 (MAGUK p55 subfamily member 3) L40387 2′-5′-oligoadenylate synthetase-like X78711 glycerol kinase D10923 putative chemokine receptor; GTP-binding protein AW006742 IMAGE-2489058 AL109730 EUROIMAGE 68600 X99699 XIAP associated factor-1 AB000115 hypothetical protein, expressed in osteoblast L13210 lectin, galactoside-binding, soluble, 3 binding protein U22970 interferon, alpha-inducible protein (clone IFI-6-16) U96721 Hermansky-Pudlak syndrome L10126 activin A receptor, type IB S62138 TLS/CHOP M33684 protein tyrosine phosphatase, non-receptor type 1 M63978 vascular endothelial growth factor X89101 tumor necrosis factor receptor superfamily, member 6 M60278 diphtheria toxin receptor (heparin-binding epidermal growth factor-like growth factor) X59770 interleukin 1 receptor, type II X04500 interleukin 1, beta D30783 epiregulin U43774 Fc fragment of IgA, receptor for

TABLE 3 Genes from Table 1 that are lower in SLE patients as compared to controls Accession No. Gene U60060 fasciculation and elongation protein zeta 1 (zygin I) AF057036 collagen-like tail subunit (single strand of homotrimer) of asymmetric acetylcholinesterase M93107 3-hydroxybutyrate dehydrogenase (heart, mitochondrial) U14575 protein phosphatase 1, regulatory (inhibitor) subunit 8 X15882 collagen VI alpha-2 C-terminal globular domain S68805 glycine amidinotransferase (L-arginine:glycine amidinotransferase) U75744 deoxyribonuclease I-like 3 AF091071 similar to S. cerevisiae RER1 AI651806 cysteine-rich motor neuron 1 AB028994 KIAA1071 protein S75168 megakaryocyte-associated tyrosine kinase X73617 T cell receptor delta locus X07730 kallikrein 3, (prostate specific antigen) AF009787 T cell receptor beta locus M21624 T cell receptor delta locus AB009598 beta-1,3-glucuronyltransferase 3 (glucuronosyltransferase I) AL021154 E2F transcription factor 2 L25444 TAF6 RNA polymerase II, TATA box binding protein (TBP)-associated factor, 80 kD AJ001383 lymphocyte antigen 94 homolog, activating NK-receptor; NK-p46, (mouse) U75370 polymerase (RNA) mitochondrial (DNA directed) AL049365 DKFZp586A0618 M16801 nuclear receptor subfamily 3, group C, member 2 M28827 CD1C antigen, c polypeptide U51712 hypothetical protein SMAP31 X66079 Spi-B transcription factor (Spi-1/PU.1 related) U11276 killer cell lectin-like receptor subfamily B, member 1 M36881 lymphocyte-specific protein tyrosine kinase M31523 transcription factor 3 (E2A immunoglobulin enhancer binding factors E12/E47) M26062 interleukin 2 receptor, beta AF026031 putative mitochondrial outer membrane protein import receptor AB011115 KIAA0543 protein AF041261 leukocyte immunoglobulin-like receptor, subfamily A (without TM domain), member 4 D55716 MCM7 minichromosome maintenance deficient 7 (S. cerevisiae) L04282 zinc finger protein 148 (pHZ-52) AJ001687 DNA segment on chromosome 12 (unique) 2489 expressed sequence AI524873 like mouse brain protein E46 U76421 adenosine deaminase, RNA-specific, B1 (homolog of rat RED1) AF031137 lymphocyte antigen 117 X59871 transcription factor 7 (T-cell specific, HMG-box) U43408 tyrosine kinase, non-receptor, 1 AB018289 KIAA0746 protein AI761647 IMAGE-2370113 M18737 granzyme A (granzyme 1, cytotoxic T-lymphocyte-associated serine esterase 3) AB023220 ubiquitin specific protease 20 W26633 melanoma antigen, family D, 1 M68892 integrin, beta 7 AJ236885 zinc finger protein 148 (pHZ-52) 2. Diagnosing Severe SLE and SLE-AIP

The invention also provides methods for diagnosing a mammal (e.g., a human) as having severe SLE or SLE-AIP. In one embodiment, a mammal can be diagnosed as having severe SLE or SLE-AIP if it is determined that the mammal contains cells that express one or more of the genes listed in Table 4 at a level that is greater than the average level of expression of the same one or more genes observed in control cells obtained from control mammals.

As described above, the mammal can be any mammal such as a human, dog, mouse, or rat. Any cell type can be isolated and evaluated. For example, peripheral blood mononuclear cells (PBMC), total white blood cells, lymph node cells, spleen cells, or tonsil cells can be isolated from a human patient and evaluated to determine if that patient contains cells that express one or more of the genes listed in Table 4 at a level that is greater than the average level of expression observed in control cells. The expression of any number of the genes listed in Table 4 can be evaluated to diagnose severe SLE or SLE-AIP. For example, the expression of one or more than one (e.g., two, three, four, five, six, seven, eight, nine, ten, 11, 12, 13, or all 14) of the genes listed in Table 4 can be used.

The expression level can be greater than or less than the average level observed in control cells obtained from control mammals. Typically, a gene can be classified as being expressed at a level that is greater than or less than the average level observed in control cells if the expression levels differ by at least 1-fold (e.g., 1.5-fold, 2-fold, 3-fold, or more than 3-fold). In addition, the control cells typically are the same type of cells as those isolated from the mammal being evaluated. In some cases, the control cells can be isolated from one or more mammals that are from the same species as the mammal being evaluated. When diagnosing severe SLE or SLE-AIP, the control cells can be isolated from mammals having mild SLE or from healthy mammals such as healthy humans who do not have SLE. Any number of control mammals can be used to obtain the control cells. For example, control cells can be obtained from one or more healthy mammals (e.g., at least 5, at least 10, at least 15, at least 20, or more than 20 control mammals).

Any method can be used to determine whether or not a specific gene is expressed at a level that is greater or less than the average level of expression observed in control cells. For example, the level of expression from a particular gene can be measured by assessing the level of mRNA expression from the gene. Levels of mRNA expression can be evaluated using, without limitation, northern blotting, slot blotting, quantitative reverse transcriptase polymerase chain reaction (RT-PCR), or chip hybridization techniques. Methods for chip hybridization assays include, without limitation, those described herein. Such methods can be used to determine simultaneously the relative expression levels of multiple mRNAs. Alternatively, the level of expression from a particular gene can be measured by assessing polypeptide levels. Polypeptide levels can be measured using any method such as immuno-based assays (e.g., ELISA), western blotting, or silver staining.

TABLE 4 Genes with expression levels that differ between SLE patients having low and high IFN scores Accession No. Gene M63835 Fc fragment of IgG, high affinity Ia, receptor for (CD64) X54486 serine (or cysteine) proteinase inhibitor, clade G (C1 inhibitor), member 1 L13210 lectin, galactoside-binding, soluble, 3 binding protein M33882 myxovirus (influenza) resistance 1, homolog of murine (interferon- inducible protein p78) AA203213 interferon-stimulated protein, 15 kDa X99699 XIAP associated factor-1 AJ225089 2′-5′-oligoadenylate synthetase-like U22970 interferon, alpha-inducible protein (clone IFI-6-16) AB000115 hypothetical protein, expressed in osteoblast AL047596 capicua homolog (Drosophila) AB006746 phospholipid scramblase 1 AL022318 similar to APOBEC1 U66711 lymphocyte antigen 6 complex, locus E X55988 ribonuclease, RNase A family, 2 (liver, eosinophil-derived neurotoxin) 3. Identifying Mammals Predisposed to Develop Severe SLE and SLE-AIP

The invention also provides methods for diagnosing a mammal (e.g., a human) as being predisposed to develop severe SLE or SLE-AIP. In one embodiment, a mammal can be diagnosed as being predisposed to develop severe SLE or SLE-AIP if it is determined that the mammal contains cells that express one or more of the genes listed in Table 4 at a level that is greater than the average level of expression of the same one or more genes observed in control cells obtained from control mammals.

As described above, the mammal can be any mammal such as a human, dog, mouse, or rat. Any cell type can be isolated and evaluated. For example, peripheral blood mononuclear cells (PBMC), total white blood cells, lymph node cells, spleen cells, or tonsil cells can be isolated from a human patient and evaluated to determine if that patient contains cells that express one or more of the genes listed in Table 4 at a level that is greater than the average level of expression observed in control cells. The expression of any number of the genes listed in Table 4 can be evaluated to diagnose a mammal as being predisposed to develop severe SLE or SLE-AIP. For example, the expression of one or more than one (e.g., two, three, four, five, six, seven, eight, nine, ten, 11, 12, 13, or all 14) of the genes listed in Table 4 can be used.

The expression level can be greater than or less than the average level observed in control cells obtained from control mammals. Typically, a gene can be classified as being expressed at a level that is greater than or less than the average level observed in control cells if the expression levels differ by at least 1-fold (e.g., 1.5-fold, 2-fold, 3-fold, or more than 3-fold). In addition, the control cells typically are the same type of cells as those isolated from the mammal being evaluated. In some cases, the control cells can be isolated from one or more mammals that are from the same species as the mammal being evaluated. When determining a mammal's susceptibility to develop severe SLE or SLE-AIP, the control cells can be isolated from mammals having mild SLE or from healthy mammals such as healthy humans who do not have SLE. Any number of control mammals can be used to obtain the control cells. For example, control cells can be obtained from one or more healthy mammals (e.g., at least 5, at least 10, at least 15, at least 20, or more than 20 control mammals).

Any method can be used to determine whether or not a specific gene is expressed at a level that is greater or less than the average level of expression observed in control cells. For example, the level of expression from a particular gene can be measured by assessing the level of mRNA expression from the gene. Levels of mRNA expression can be evaluated using, without limitation, northern blotting, slot blotting, quantitative reverse transcriptase polymerase chain reaction (RT-PCR), or chip hybridization techniques. Methods for chip hybridization assays include, without limitation, those described herein. Such methods can be used to determine simultaneously the relative expression levels of multiple mRNAs. Alternatively, the level of expression from a particular gene can be measured by assessing polypeptide levels. Polypeptide levels can be measured using any method such as immuno-based assays (e.g., ELISA), western blotting, or silver staining.

4. Arrays

The invention also provides nucleic acid arrays. The arrays provided herein can be two-dimensional arrays, and can contain at least 10 different nucleic acid molecules (e.g., at least 20, at least 30, at least 50, at least 100, or at least 200 different nucleic acid molecules). Each nucleic acid molecule can have any length. For example, each nucleic acid molecule can be between 10 and 250 nucleotides (e.g., between 12 and 200, 14 and 175, 15 and 150, 16 and 125, 18 and 100, 20 and 75, or 25 and 50 nucleotides) in length. In addition, each nucleic acid molecule can have any sequence. For example, the nucleic acid molecules of the arrays provided herein can contain sequences that are present within the genes listed in Tables 1, 2, 3, and/or 4.

Typically, at least 25% (e.g., at least 30%, at least 40%, at least 50%, at least 60%, at least 75%, at least 80%, at least 90%, at least 95%, or 100%) of the nucleic acid molecules of an array provided herein contain a sequence that is (1) at least 10 nucleotides (e.g., at least 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, or more nucleotides) in length and (2) at least about 95 percent (e.g., at least about 96, 97, 98, 99, or 100) percent identical, over that length, to a sequence present within a gene listed in Tables 1, 2, 3, and/or 4. For example, an array can contain 100 nucleic acid molecules located in known positions, where each of the 100 nucleic acid molecules is 100 nucleotides in length while containing a sequence that is (1) 30 nucleotides is length, and (2) 100 percent identical, over that 30 nucleotide length, to a sequence of one of the genes listed in Table 4. Thus, a nucleic acid molecule of an array provided herein can contain a sequence present within a gene listed in Tables 1, 2, 3, and/or 4, where that sequence contains one or more (e.g., one, two, three, four, or more) mismatches.

The nucleic acid arrays provided herein can contain nucleic acid molecules attached to any suitable surface (e.g., plastic or glass). In addition, any method can be use to make a nucleic acid array. For example, spotting techniques and in situ synthesis techniques can be used to make nucleic acid arrays. Further, the methods disclosed in U.S. Pat. Nos. 5,744,305 and 5,143,854 can be used to make nucleic acid arrays.

The invention will be further described in the following examples, which do not limit the scope of the invention described in the claims.

EXAMPLES Example 1 Identifying Genes that can be Used to Diagnose SLE

PBMCs were collected from 48 SLE patients and 42 healthy, age- and gender-matched control individuals. All patients had physician-verified SLE and met at least four of the eleven ACR criteria for lupus. The average, age of SLE patients was 45±11 years, and the average age of controls was 34±13 years. Each PBMC sample contained monocytes/macrophages, B and T lymphocytes, and natural killer cells.

For the first 11 patients and 11 controls, poly A⁺ mRNA was extracted from the collected PBMC samples. Briefly, 60 mL of peripheral blood was drawn into a heparinized syringe. Whole blood was layered over an equal volume of Histopaque and centrifuged at 400×g for 30 minutes at 25° C. Plasma was harvested and stored at −80° C. PBMCs were harvested and washed twice in 1×PBS, and the mRNA was isolated using a FastTrack mRNA isolation kit (Invitrogen, Carlsbad, Calif.).

For the next 37 patients and 31 controls, total RNA was extracted from the collected PBMC samples. Briefly, peripheral blood was drawn into CPT tubes (Becton-Dickinson, Franklin Lakes, N.J.), and plasma and PBMCs were collected according to manufacturer's protocol. Plasma was stored at −80° C., and total RNA was isolated from PBMCs using Trizol (Gibco-BRL, Invitrogen, Carlsbad, Calif.) followed by an RNeasy cleanup (Qiagen, Valencia, Calif.).

About 5 to 10 μg of total RNA or about 100-200 ng of poly A⁺ RNA was used to prepare biotinylated cRNA for hybridization using the standard Affymetrix protocol (Expression Analysis Technical Manual, Affymetrix, Inc., 2000). Briefly, RNA was converted to first strand cDNA using a T7-linked oligo(dT) primer (Genset, La Jolla, Calif.) followed by second strand synthesis (Gibco-BRL). The dscDNA was then used as template for labeled in vitro transcription reactions using biotinylated ribonucleotides (Enzo, Farmingdale, N.Y.). Fifteen μg of each labeled cRNA was hybridized to Affymetrix U95A GeneChips (Affymetrix, Santa Clara, Calif.) using standard conditions in an Affymetrix fluidics station.

After chip hybridization and initial data analysis, the expression values for 10,260 genes represented on the chip were compared between SLE patients and controls using a non-paired Student's T-test.

Affymetrix Microarray Suite (MAS) 4.0 software was used to generate expression values (referred to as an “average difference;” AD) for each gene. Each chip was scaled to an overall intensity of 1500 to correct for minor differences in overall chip hybridization intensity and to allow comparison between chips. A threshold of 20 AD units was assigned to any gene that was called “Absent” by MAS. In addition, any gene with an AD less than 20 was assigned this threshold. Data from U95Av1 and U95Av2 chips were aligned by discarding the 51 probe sets that were not present on both chips. The analysis identified 161 unique genes that were differentially expressed using the following criteria: p<0.001, fold-change >1.5, mean expression value difference >100 units.

Despite the use of the same oligo(dT) primer for cDNA synthesis, consistent differences between the raw AD values obtained from polyA⁺ RNA and total RNA samples were noted that were not corrected by chip scaling. Furthermore, each dataset (i.e., polyA⁺ RNA and total RNA) showed similar differential gene expression between the respective groups of patients and controls. For example, the initial 11/11 dataset identified a larger than expected number of interferon-regulated genes. A gene-by-gene scaling approach thus was employed so that the two datasets could be combined and examined together. The scaling strategy was based on the assumption that the mean expression level (mean AD) of genes between the two control groups (total vs. polyA⁺ RNA) should be equal. For each gene, the mean of the two control groups was compared to generate the gene-specific scaling factor. The polyA⁺ samples were corrected by the scaling factor so that the means of the two control groups (total and polyA⁺) were identical. This scaled dataset then was used for all subsequent analysis.

Identification of stress response genes: During the course of collecting and analyzing the various samples, it was determined that many genes in peripheral blood cells undergo striking stress responses following incubation ex vivo, even during somewhat limited periods of time (i.e., less than 1 hour). A formal experiment was designed and performed to identify those genes that were regulated by incubation of cells ex vivo. Changes in global gene expression were examined using whole blood after overnight shipment by a commercial carrier. This study utilized samples from eight healthy control individuals. Approximately 30 mL of blood was drawn into four CPT tubes. PBMCs were isolated from two tubes and resuspended in RNAlater (Ambion, Austin, Tex.). RNAlater immediately lyses the cells and protects the RNA from degradation, thus providing an accurate profile of gene expression immediately ex vivo. The RNA preserved in RNAlater and the two CPT tubes with whole blood were shipped by overnight carrier. Total RNA was extracted and prepared for hybridization as described above. Thus, global gene expression profiles were obtained from both a fresh blood sample and from blood shipped overnight, with both samples coming from the same blood draw.

Data were analyzed using MAS 4.0 and each chip was scaled to 1500. Absent and low expression values were assigned a threshold of 20 AD units as described above. A paired T-Test was used to compare the gene expression profiles of fresh blood vs. blood shipped by overnight carrier. Based on this experiment, 2076 genes were identified that displayed significant changes in expression under these environmental stresses (p<0.01). These genes, many of which are involved in various cell stress pathways, were excluded from further analysis due to the high level of variability that they exhibited.

Comparison analyses: The individual gene expression levels of SLE patients and controls were compared using an unpaired Student's T-test. Genes selected for further analysis met the following three criteria:

(i) p<0.001 by unpaired T-test,

(ii) change in expression of at least 1.5-fold when comparing the means of the two groups, and

(iii) difference in expression of at least 100 when comparing the means of the two groups.

Overall, 484 genes were differentially expressed at the p<0.001 level, while 178 genes were both differentially expressed at the p<0.001 level and showed mean AD values that differed by more than 1.5-fold. The final dataset of 161 individual genes (represented by 171 Genbank accession numbers) met all three criteria. These genes, which demonstrated differential expression between SLE patients and normal controls, are listed in Table 1.

Expression values for each of the 161 genes were converted to “fold-differences” by dividing each value by the mean of the control expression values. Unsupervised hierarchical clustering then was applied to the dataset. Hierarchical clustering was performed using Cluster and visualized using TreeView (M. Eisen, Stanford; available on the internet at rana.lbl.gov). This analysis identified gene expression patterns that differentiated most SLE patients from healthy controls. Thirty-seven of the 48 SLE patients clustered tightly together, while 11 of the patients co-clustered with controls. Six of the 42 control subjects clustered together with the large group of patients.

Most (124 of 161, 77%) of the genes that best distinguished SLE from control PBMCs were expressed at higher levels in SLE patients than in normal subjects. These are presented in Table 2. A number of these genes have known or suspected roles in the immune system. For example, many SLE patients were found to overexpress mRNA for the following cell surface markers: TNFR6 (Fas/CD95), a death receptor; ICAM-1 (CD54), an adhesion molecule; CD69, an activation antigen; and complement receptor 1. Of interest, three different Fc receptors were expressed at elevated levels: the Fe receptor for IgA (FCAR, CD89), and the IgG receptors FcRγIIA (CD32) and FcRγI (CD64). Three molecules in the inflammatory IL-1 cytokine pathway—IL-1β, the IL-1 receptor II (IL-1RII), and the IL-1 receptor antagonist—also were generally overexpressed. Interestingly, Jagged 1, a ligand for Notch 1 located in the SLE susceptibility interval on chromosome 20p, also was overexpressed in some patients. Other notable genes that were overexpressed in SLE patients include the signaling molecules MAP3K-8, RAB27, interleukin-6 signal transducer, the transcription factors v-ets 2, MADS box transcription factor 2, and the estrogen responsive zinc finger protein 147.

A number of genes were expressed at lower levels in patients than controls. These are presented in Table 3, and included T cell genes such as Lck, TCR delta, and TCR beta. Flow cytometry of freshly stained PBMCs was used to confirm that there was a T cell lymphopenia in many of the patients (i.e., about a 20% decrease, on average, in percentage of CD3⁺ T cells). The patients also demonstrated a significant increase in the percentage of monocytes, as compared to the percentage of monocytes in controls. Specifically, PBMC populations from SLE patients (n=18) contained 52% T cells, 5% B cells, 28% monocytes/macrophages, and 15% NK cells. PMBC populations from control subjects (n=28) contained 65% T cells, 6% B cells, 13% monocytes/macrophages, and 16% NK cells. The percentages of T cells (p=0.014) and monocytes (p=0.00001) thus differed between SLE and controls. These differences in baseline cell populations clearly contribute to some of the differences in gene expression observed, and highlight the importance of documenting cell percentages in mixed cell populations.

Identification of IFN-regulated genes: One of the most striking mRNA clusters contained several genes previously identified as being interferon-regulated (Der et al. (1998) Proc. Natl. Acad. Sci. U.S.A. 95:15623). Interferons are highly active cytokines important for maintaining viral immunity (IFN-α and IFN-β) and for mediating TH1 immune responses (IFN-γ). Genes in this cluster were up-regulated in about half of the SLE patients, and were expressed at low levels in most of the control subjects.

Experiments were conducted to examine the extent to which the genes in this cluster could be regulated in PBMCs by IFN treatment in vitro. Peripheral blood was drawn from each of four healthy control individuals. PBMCs were isolated over Lymphocyte Separation Medium (Mediatech Cellgro, Herndon, Va.) according to the manufacturer's protocol. After the last wash, cells were resuspended in complete media (RPMI1640, 10% heat inactivated FBS, 2 mM L-glutamine, pen/strep) at a final concentration of 2×10⁶ cells/mL. PBMCs were cultured for six hours at 37° C. with the following additions:

(i) PBS+0.1% BSA control,

(ii) IFN-α and IFN-β (R&D Systems, Minneapolis, Minn.), each at 1000 U/mL in PBS+0.1% BSA, and

(iii) IFN-γ (R&D Systems, Minneapolis, Minn.), 1000 U/mL in PBS+0.1%.

Following the incubation, total RNA was isolated, and cRNA probes were prepared for chip hybridization. Data were analyzed in MAS 4.0, and all chips were scaled to 1500. Absent and low expression values were assigned a threshold of 20 AD units as described above. Genes that met both of the criteria below in all four experiments were identified as IFN-regulated:

(i) change in expression of at least 2-fold when compared to untreated control, and

(ii) difference in expression of at least 500 AD units when compared to untreated control.

Changes in gene expression following IFN treatment were assessed relative to a six-hour control culture. This analysis identified 286 genes that demonstrated more than a 2-fold change in expression from baseline, and an absolute mean difference in the level of expression of greater than 500 units. The induction of many known IFN-regulated genes, such as Stat1, myxovirus resistance 1 (Mx-1), and ISGF-3, validated the approach. Using this list of IFN-regulated genes, 13 of 14 unique genes in the cluster were identified as bona fide IFN-regulated transcripts. Overall, 23 of the 161 genes (14.3%) were found to be IFN-regulated, compared with 7 genes (4.3%) that would have been expected by chance alone. The overrepresentation of interferon-regulated genes in the list of transcripts that best discriminated SLE patients from controls was consistently observed when a variety of different filters were used to define both IFN-regulated and SLE genes.

The mRNA levels of the IFNs themselves were not significantly different between patients and controls. Plasma/serum IFN-γ and IFN-α proteins were measured by ELISA (Pierce Endogen, Rockford, Ill.). IFN-γ was undetectable in all samples (less than 25 pg/mL). IFN-α was detectable in only two patients (26 and 29 pg/mL) and one control subject (56 pg/mL).

An IFN “score” was calculated for each patient and control, based on expression of genes in the IFN cluster. Scores were calculated by first normalizing the expression values within each row of genes so that the maximum value in any row was 1.0. Then the columns (samples) were summed to obtain the score. The IFN score (mean±SD) for patients was 3.7±2.6, compared to controls 1.5±0.5, p=4.2×10⁻⁷. Approximately half of the SLE patients exhibited an elevated IFN score, while the others had scores indistinguishable from controls (FIG. 1).

The lupus patient population was divided into two groups, with the IFN-high group containing the 24 patients with the highest IFN scores, and the IFN-low group containing the 24 patients with the lowest scores. Differences in gene expression were examined. Table 4 contains a list of the genes that displayed differential expression between the IFN-high and IFN-low groups. All of the genes listed in Table 4 were expressed at a greater level in the IFN-high group that in the IFN-low group.

Studies then were conducted to determine whether the IFN gene expression signature correlated with clinical features of SLE. SLE typically is diagnosed using eleven criteria developed by the ACR (Hochberg (1997) Arthritis Rheum. 40:1725). These criteria span the clinical spectrum of SLE and include skin criteria (malar rash, oral ulcers, photosensitivity, and discoid rash), systemic criteria (pleuritis or pericarditis, arthritis, renal disease, or CNS involvement), and laboratory criteria (cytopenias, anti-dsDNA or anti-phospholipid Abs, and antinuclear antibodies). A patient must meet four of these criteria to be classified as having definite SLE. The number of SLE criteria met by each patient was plotted against his or her IFN score (FIG. 2). This analysis revealed that the IFN score was correlated with the number of SLE criteria displayed in each patient.

In a similar analysis, the clinical features of the 24 SLE patients with the highest IFN scores (IFN-high) were compared to the clinical features of the 24 SLE patients with the lowest scores (IFN-low). As depicted in FIG. 3, patients in the IFN-high group had a significantly higher number of SLE criteria (6.8±1.3) than those in the IFN-low group (5.7±1.1; p=0.004). Patients in the IFN-high group also showed a trend towards being diagnosed with SLE at an earlier age (25±12 compared with 30±13 years; p=0.192). Importantly, 15 of 24 patients (63%) in the IFN-high group fulfilled the ACR criteria for involvement of kidneys and/or the CNS, the most serious complications of lupus, compared with 5 of 24 patients (21%) in the IFN-low group (FIG. 4). In addition, 18 of 24 IFN-high patients (75%) showed hematologic involvement in their disease (severe leukopenia, hemolytic anemia or thrombocytopenia), compared with only 5 of 24 IFN-low patients (21%). An elevated interferon score thus correlated with the more severe manifestations of SLE.

The hypothesis that IFNs are important in the pathogenesis of lupus is supported by a number of observations. Mice transgenic for IFN-γ develop lupus-like autoimmunity (Seery et al. (1997) J. Exp. Med. 186:1451), and lupus-prone NZB/NZW F1 mice treated with anti-IFN-γ Abs or bred onto the IFN-γ^(−/−) background show amelioration of disease (Jacob et al. (1987) J. Exp. Med. 166:798; and Balomenos et al. (1998) J. Clin. Invest. 101:364). The interferon-inducible gene IFI-202 has been identified as an SLE gene within the Nba2 SLE locus on mouse chromosome 1, NZB mice, the parental strain for this locus, show constitutively high expression of this transcription factor (Rozzo et al. (2001) Immunity 15:435). In humans, elevated levels of IFN-α have been reported in the sera of some SLE patients (for review see Ronnblom and Alm (2001) J. Exp. Med. 194:59), and a significant percentage of individuals treated with IFN-α for viral hepatitis develop lupus-related autoantibodies (Fukuyama et al. (2000) Am. J. Gastroenterol. 95:310). Finally, IFN-α in the sera of some pediatric SLE patients induces maturation of monocytes into highly active antigen-presenting plasmacytoid dendritic cells (Blanco et al. (2001) Science 294:1540).

While genes in IFN-signaling pathways exhibited dysregulated expression in some lupus patients, the mRNA levels of the IFNs themselves were not significantly different between patients and controls. IFN-γ protein was not detectable by ELISA in any patient or control sample, and IFN-α was detectable in only 2 of 48 patients and 1 of 42 controls. Thus, other cytokines that utilize Jak/Stat signaling pathways downstream of their receptors, such as IL-4, IL-13, or IL-2 (Hirano et al. (2000) Oncogene 19:2548), could contribute to the gene expression patterns observed.

OTHER EMBODIMENTS

It is to be understood that while the invention has been described in conjunction with the detailed description thereof, the foregoing description is intended to illustrate and not limit the scope of the invention, which is defined by the scope of the appended claims. Other aspects, advantages, and modifications are within the scope of the following claims. 

1. A method for diagnosing severe systemic lupus erythematosus in a human, said method comprising: (a) providing a biological sample comprising peripheral blood mononuclear cells (PBMC) from a human having or suspected of having systemic lupus erythematosus, (b) measuring mRNA expression of a gene in said sample, wherein said gene is interferon, alpha-inducible protein (clone IFI-6-16), and (c) diagnosing said human as having severe systemic lupus erythematosus if the level of said mRNA expression is greater in said sample than in a corresponding control sample, or diagnosing said human as not having severe systemic lupus erythematosus if the level of said mRNA expression is not greater in said sample than in a corresponding control sample.
 2. The method of claim 1, wherein said mRNA expression is measured in a blood sample from said human.
 3. The method of claim 1, wherein said mRNA expression is measured in PBMC from said human.
 4. The method of claim 1, wherein said mRNA expression is measured in monocytes from said human.
 5. A method for assessing the predisposition of a human to develop severe systemic lupus erythematosus, said method comprising: (a) providing a biological sample comprising PBMC from a human, (b) measuring mRNA expression of a gene in said sample, wherein said gene is interferon, alpha-inducible protein (clone IFI-6-16), and (c) classifying said human as having a predisposition to develop severe systemic lupus erythematosus if the level of said mRNA expression is at least 2-fold greater in said sample than in a corresponding control sample, or classifying said human as not having a predisposition to develop severe systemic lupus erythematosus if the level of said mRNA expression is not at least 2-fold greater in said sample than in said control sample.
 6. The method of claim 5, wherein said mRNA expression is measured in a blood sample from said human.
 7. The method of claim 5, wherein said mRNA expression is measured in PBMC from said human.
 8. The method of claim 5, wherein said mRNA expression is measured in monocytes from said human.
 9. The method of claim 1, wherein the level of said mRNA expression is at least 3-fold greater in said sample than in said corresponding control sample.
 10. The method of claim 5, wherein the level of said mRNA expression is at least 3-fold greater in said sample than in said corresponding control sample. 